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Lifelong aspect extraction from big data: knowledge engineering
| Content Provider | Paperity |
|---|---|
| Author | Khalid, Shehzad Khan, M. Taimoor Durrani, Mehr Aziz, Furqan |
| Abstract | Traditional machine learning techniques follow a single shot learning approach. It includes all supervised, semi-supervised, transfer learning, hybrid and unsupervised techniques having a single target domain known prior to analysis. Learning from one task is not carried to the next task, therefore, they cannot scale up to big data having many unknown domains. Lifelong learning models are tailored for big data having a knowledge module that is maintained automatically. The knowledge-base grows with experience where knowledge from previous tasks helps in current task. This paper surveys topic models leading the discussion to knowledge-based topic models and lifelong learning models. The issues and challenges in learning knowledge, its abstraction, retention and transfer are elaborated. The state-of-the art models store word pairs as knowledge having positive or negative co-relations called must-links and cannot-links. The need for innovative ideas from other research fields is stressed to learn more varieties of knowledge to improve accuracy and reveal more semantic structures from within the data. |
| Starting Page | 5 |
| File Format | HTM / HTML |
| DOI | 10.1186/s40294-016-0018-7 |
| Issue Number | 1 |
| Journal | Complex Adaptive Systems Modeling |
| Volume Number | 4 |
| e-ISSN | 21943206 |
| Language | English |
| Publisher | Springer Berlin Heidelberg |
| Publisher Date | 2016-03-31 |
| Access Restriction | Open |
| Subject Keyword | Lifelong topic models Automatic knowledge-based models Knowledge engineering Big textual data analysis Aspect extraction Knowledge-based topic models |
| Content Type | Text |
| Resource Type | Article |
